Performance Study of Convolutive BSS Algorithms Applied to the Electrocardiogram of Atrial Fibrillation

  • Carlos Vayá
  • José Joaquín Rieta
  • César Sánchez
  • David Moratal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3889)


Atrial Fibrillation (AF) is one of the atrial cardiac arrythmias with highest prevalence in the elderly. In order to use the electrocardiogram (ECG) as a noninvasive tool for AF analysis, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, Blind Source Separation (BSS) techniques are able to perform a multi-lead analysis of the ECG with the aim to obtain a set of independent sources where the AA is included. Two different assumptions on the mixing model in the human body can be done. Firstly, the instantaneous mixing model can be assumed in spite of the inaccuracy of this approximation. Secondly, the convolutive model is a more realistic model where weighted and delayed contributions in the generation of the electrocardiogram signals are considered. In this paper, a comparison between the performance of both models in the extraction of the AA in AF episodes is developed by analyzing the reults of five distinct BSS algorithms.


Atrial Fibrillation Independent Component Analysis Blind Source Separation Atrial Activity Ventricular Activity 
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  1. 1.
    Fuster, V., Ryden, L., Asinger, R.W., et al.: CC/AHA/ESC guidelines for the management of patients with atrial fibrillation. Journal of the American College of Cardiology 38(4), 1266/I–1266/LXX (2001) Google Scholar
  2. 2.
    Rieta, J.J., Castells, F., Sänchez, C., Zarzoso, V.: Atrial activity extraction for atrial fibrillation analysis using blind source separation. IEEE Transations of Biomedical Engineeringy 51(7), 1176–1186 (2004)CrossRefGoogle Scholar
  3. 3.
    Rieta, J.J., Castells, F., Sánchez, C., Moratal-Pérez, D., Millet, J.: Bioelectric model of atrial fibrillation: applicability of blind source separation techniques for atrial activity estimation in atrial fibrillation episodes. IEEE Computers in Cardiology 30, 525–528 (2003)CrossRefGoogle Scholar
  4. 4.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, Inc., Chichester (2001)CrossRefGoogle Scholar
  5. 5.
    Sanchis, J.M., Castells, F., Rieta, J.J.: Convolutive acoustic mixtures approximation to an instantaneous model using a stereo boundary microphone configuration. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 816–823. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Lambert, R.H.: Multichanel Blind Deconvolution: FIR matrix algebra and separation of multipath mixtures. PH.D, University of Southern California (1996)Google Scholar
  7. 7.
    Ikeda, S., Murata, N.: A method of blind separation on temporal structure of signals. In: Proceedings of The Fifth International Conference on Neural Information Processing (ICONIP 1998), Kitakyushu, Japan, pp. 737–742 (1998)Google Scholar
  8. 8.
    Asano, F., Ikeda, S., Ogawa, M., Asoh, H., Kitawaki, N.: A combined approach of array processing and independent component analysis for blind separation of acoustic signals. In: Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing, Salt Lake City, USA (2001)Google Scholar
  9. 9.
    Schobben, D.W.E., Sommen, P.C.W: A new convolutive blind signal separation algortithm based on second order statistics. In: Proc. Int. Conf. on Signal and Image Processing, pp. 564–569 (1998)Google Scholar
  10. 10.
    Plonsey, R., Heppner, D.B.: Considerations of quasi-stationarity in electrophysiological systems. Bulletin of Mathematical Biophysics 29(4), 657–664 (1967)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Carlos Vayá
    • 1
  • José Joaquín Rieta
    • 1
  • César Sánchez
    • 2
  • David Moratal
    • 1
  1. 1.Bioengineering, Electronics, Telemedicine and Medical Computer Science Research GroupValencia University of TechnologyGandía (Valencia)Spain
  2. 2.Innovation in BioengineeringCastilla-La Mancha UniversityCuencaSpain

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